Oncotarget

Meta-Analysis:

Association of miR-196a2 rs11614913 and miR-499 rs3746444 polymorphisms with cancer risk: a meta-analysis

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Oncotarget. 2017; 8:114344-114359. https://doi.org/10.18632/oncotarget.22547

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Wanjun Yan, Xiaoyan Gao and Shuqun Zhang _

Abstract

Wanjun Yan1, Xiaoyan Gao1 and Shuqun Zhang1

1Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi 710004, P.R. China

Correspondence to:

Shuqun Zhang, email: zhangshuqun1971@aliyun.com

Keywords: miRNA, single nucleotide polymorphism, cancer susceptibility, meta-analysis

Received: March 08, 2017     Accepted: August 29, 2017     Published: November 20, 2017

ABSTRACT

Background: MicroRNAs (miRNAs) are small non-coding RNA molecules, which participate in diverse biological processes and may regulate tumor suppressor genes or oncogenes. Rs11614913 in miR-196a2 and rs3746444 in miR-499 are shown to associate with increased/decreased cancer risk. This meta-analysis was performed to systematically assess the overall association.

Materials and Methods: We searched Pubmed, Web of Knowledge, EMBASE, Chinese National Knowledge Infrastructure (CNKI) databases until December 2016 to identify eligible studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the strength of the associations.

Results: We assessed published studies of the association between these microRNA polymorphisms and cancer risk from 56 studies with 21958/26436 cases/controls for miR-196a2 and from 37 studies with 13759/17946 cases/controls for miR-499. The results demonstrated that miR-196a2 rs11614913 was significantly associated with a decreased cancer risk, in particular with a decreased risk for colorectal cancer and gastric cancer, or for Asian population subgroup. In addition, miR-499 rs3746444 polymorphism was observed as a risk factor for cancers, in particular, for breast cancer, or for in the Asian population.

Conclusions: Our meta-analysis suggests that the rs11614913 most likely contributes to decreased susceptibility to cancer, especially in Asians and colorectal cancer and gastric cancer, and that the rs3746444 may increase risk for cancer. Furthermore, more well-designed studies with large sample size are still necessary to further elucidate the association between polymorphisms and different kinds of cancers risk.


INTRODUCTION

Cancer is reportedly one of the major causes of death among human worldwide [1]. According to GLOBOCAN 2012 report, there were 14.1 million new cases and 8.2 million deaths in 2012 [2]. Recently reported, as a very complex genetic disease, the mechanism of cancer has not been completely elucidated. Moreover, studies have suggested that cancer development results from gene-environment interactions [3].

MicroRNAs (miRNAs) are a class of endogenous small single-stranded, long non-coding RNA molecules, which play critical roles in a extensive range of biologic and pathologic processes, especially in carcinogenesis [45]. Accumulating studies indicates that a single miRNA cantarget 200 genes, and approximately 20% of human genes are regulated by the mature miRNA molecules [6]. More than half of miRNAs genes are located in cancer-related genomic regions, indicating that these miRNAs may play a more important key role in the etiology, tumorigenesis, development and prognosis of human cancers than previous research [7].

Single nucleotide polymorphisms (SNPs) occurring in the miRNA gene region may influence the function of specific miRNA molecules and the genetic variation, which are associated with cancer susceptibility through altering miRNA molecules expression [89]. Recently, miR-196a2C.T (rs11614913) and miR-499 A.G (rs3746444) have been reported to demonstrate the association with malignant tumors susceptibility [1017]. For instance, Min et al. [11] demonstrates that the miRNA variants could affect the development of colorectal cancer in the Korean, while Georgeh et al. [17]. showed miR-196a2C.T (rs11614913)and miR-499 A.G (rs3746444) revealed significant risk for developing prostate cancer in North Indian. However, the consequences of these relevant studies remain incomprehensive and controversial. To the best of our knowledge, there is no systematic and comprehensive reports or studies regarding the impact of miR196a2 and miR-499 variants on overall cancer risk in world wide population. Hence, we performed a meta-analysis to clarify the associaton between the miR-196a2C.T (rs11614913)and miR-499 A.G (rs3746444) polymorphisms with cancer susceptibility.

MATERIALS AND METHODS

Publication search

We carried out a search in PubMed, ISI Web of Knowledge, EMBASE, Chinese National Knowledge Infrastructure(CNKI) databases for all relevant reports using the key words “microRNA 192” OR “microRNA-192” OR “miR-192” OR “rs11614913” OR “microRNA 499” OR “microRNA-499” OR “miR-499” OR “ rs3746444”) AND (“polymorphism” OR “SNP” OR “variation” OR “locus” OR “mutation”) AND (“cancer” OR “tumor” OR “malignancy” OR “carcinoma” OR “neoplasm”(updated to Dec 30, 2016). The search was limited to English language papers and human subject studies. We evaluated potentially relevant publications by examining their titles and abstracts, thereafter all studies matching the eligible inclusion criteria were retrieved.

Selection criteria

The following criteria were used to select studies for further meta-analysis: (a) about the miR-196a2 rs11614913/miR-499 rs3746444 polymorphisms and cancer risk, (b) full-text study, (c) from a case-control designed study, (d) genotype frequencies available, (e) sufficient published data for estimating an odds ratio (OR) with 95% confidence interval (CI).

Accordingly, the following exclusion criteria were also used: (a) the design of the experiments were not case-control studies; (b) the source of cases and controls, and other essential information were not provided; (c) reviews and duplicated publications.

Data extraction

All data were independently abstracted in duplicate by two investigators (Yan and Zhang) using a standard protocol and data-collection form according to the inclusion criteria listed above. The following information was sought from each publication: the first author’s name, year of publication, country of origin, ethnicity, cancer type, source of control (population- or hospital-based controls), genotyping method and number of cases and controls miR-196a2 C/T and/or miR-499 G/A genotypes, respectively (Table 1). Different ethnicity descents were categorized as Caucasian and Asian.

Table 1: Summary of published studies included

Author

Year

Race

Cancer type

Control

Method

Case/control

Polymorphism site

1

Ahn [22]

2012

Asian

gastric cancer

PB

PCR–RFLP

461/477

rs11614913, rs3746444

2

Alshatwi [23]

2012

Asian

Breast Cancer

PB

PCR–RFLP

89/100

rs11614913, rs3746444

3

B.Zhou [24]

2011

Asian

Cervical Squamous Cell Carcinoma

PB

PCR–RFLP

226/309

rs11614913, rs3746444

4

Bansal [25]

2014

Asian

breast cancer

PB

PCR–RFLP

121/164

rs11614913, rs3746444

5

Behnaz [26]

2016

Caucasian

HCC

PB

PCR

103/432

rs11614913

6

Brajušković [27]

2015

Caucasian

prostate cancer

PB

PCR–RFLP

355/312

rs11614913, rs3746444

7

Catucci [28]

2012

Caucasian

breast cancer

PB

PCR–RFLP

1894/2760

rs11614913, rs3746444

8

cheng [29]

2015

Asian

gastric cancer

HB

MassARRAY

363/969

rs3746444

9

Chu [30]

2012

Asian

Oral Cancer

PB

PCR–RFLP

470/425

rs11614913, rs3746444

10

D. Li [31]

2015

Asian

HCC

PB

RT-PCR

184/184

rs3746444

11

Dai [32]

2016

Asian

breast cancer

HB

MassARRAY

560/583

rs11614913, rs3746444

12

Deng [33]

2015

Asian

bladder cancer

PB

PCR–RFLP

159/298

rs11614913, rs3746444

13

Dominik [34]

2011

Caucasian

Breast Cancer

PB

PCR–RFLP

187/171

rs11614913

14

Dou [35]

2010

Asian

glioma

PB

PCR–RFLP

643/656

rs11614913

15

Eman [36]

2016

Caucasian

Hepatic/Renal Cancer

PB

RT-PCR

65/150

rs11614913, rs3746444

16

Gu [37]

2013

Asian

esophageal cancer

PB

PCR–RFLP

380/380

rs11614913, rs3746444

17

H. Chen [38]

2011

Asian

colorectal cancer

HB

PCR–LDR

126/407

rs11614913

18

H. Zhao [39]

2016

Asian

breast cancer

HB

RT-PCR

114/114

rs11614913

19

Hashemi [40]

2016

Asian

prostate cancer

PB

PCR–RFLP

169/182

rs11614913, rs3746444

20

Hikmet [41]

2011

Caucasian

HCC

PB

PCR–RFLP

222/222

rs3746444

21

Hong [42]

2011

Asian

Lung Cancer

HB

PCR–RFLP

406/428

rs11614913

22

Hu [43]

2013

Asian

Glioma

HB

PCR–RFLP

680/690

rs11614913, rs3746444

23

J. Shi [44]

2015

Asian

gastric cancer

PB

RT-PCR

448/452

rs3746444

24

Kim [45]

2012

Asian

colorectal cancer

PB

PCR–RFLP

201/159

rs11614913, rs3746444

25

Kim [46]

2011

Asian

lung cancer

PB

PCR–RFLP

654/640

rs11614913

26

Kou [47]

2014

Asian

HCC

PB

PCR–RFLP

271/532

rs11614913, rs3746444

27

Kshitij [48]

2010

Asian

gallbladder cancer

PB

PCR–RFLP

230/230

rs11614913, rs3746444

28

Kuo [49]

2014

Asian

HCC

PB

PCR–RFLP

188/377

rs11614913, rs3746444

29

Li [50]

2015

Asian

non-Hodgkin lymphoma

PB

RT-PCR

318/320

rs11614913

30

Linhare [51]

2012

Caucasian

Breast cancer

PB

TaqMan

388/388

rs11614913

31

LIU [52]

2010

Asian

HCC

HB

PCR–RFLP

310/222

rs11614913

32

Lv [53]

2013

Asian

colorectal cancer

PB

PCR–RFLP

353/540

rs11614913

33

M. Zhang [54]

2012

Asian

Breast Cancer

PB

PCR–RFLP

252/248

rs11614913

34

Masaaki [55]

2010

Asian

gastric cancer

PB

PCR–RFLP

552/697

rs11614913, rs3746444

35

Min [11]

2012

Asian

Colorectal Cancer

PB

PCR–RFLP

446/502

rs11614913, rs3746444

36

Morales [56]

2016

Caucasian

Breast cancer

PB

TaqMan

440/807

rs11614913

37

N. Wang [57]

2014

Asian

ESCC

PB

PCR-LDR

597/597

rs11614913

38

Ni [58]

2015

Asian

endometrial/ovarian cancer

PB

PCR

141/100

rs11614913, rs3746444

39

Omrani [59]

2014

Asian

breast cancer

PB

PCR

236/203

rs11614913, rs3746444

40

P. Dikaiakos [60]

2015

Caucasian

colorectal cancer

PB

PCR–RFLP

157/299

rs11614913

41

P. Li [61]

2014

Asian

Nasopharyngeal Carcinoma

PB

RT-PCR

1020/1006

rs11614913

42

P. Qi [62]

2015

Asian

breast cancer

PB

PCR–RFLP

321/290

rs11614913, rs3746444

43

Panagiotis [63]

2014

Caucasian

gastric cancer

HB

PCR–RFLP

163/480

rs11614913

44

Pavlakis [64]

2013

Caucasian

pancreatic cancer

HB

PCR–RFLP

93/122

rs11614913

45

Peng [65]

2010

Asian

gastric cancer

HB

PCR–RFLP

231/213

rs11614913

46

Qi [66]

2011

Asian

HCC

PB

PCR–LDR

361/391

rs11614913

47

Qu [67]

2014

Asian

ESCC

HB

PCR–RFLP

381/426

rs11614913

48

Rama [68]

2010

Asian

Bladder Cancer

PB

PCR–RFLP

212/250

rs11614913, rs3746444

49

Renata [69]

2012

Caucasian

colorectal cancer

PB

PCR–RFLP

197/212

rs11614913

50

Roshni [70]

2014

Asian

oral cancer

PB

PCR–RFLP

451/452

rs11614913

51

Serena [71]

2011

Caucasian

Lung Cancer

PB

RT-PCR

101/129

rs11614913, rs3746444

52

Shen [72]

2015

Asian

ESCC

PB

Hapmap

1400/2185

rs11614913, rs3746444

53

Sushma [73]

2015

Caucasian

Oral Squamous Cell Carcinoma

PB

PCR–RFLP

100/102

rs11614913, rs3746444

54

Tian [74]

2009

Asian

lung cancer

PB

PCR–RFLP

1058/1035

rs11614913, rs3746444

55

Wang [75]

2014

Asian

HCC

PB

PCR–RFLP

152/304

rs3746444

56

Wu [76]

2013

Asian

gastric cancer

PB

PCR–RFLP

200/211

rs3746444

57

Yan [77]

2015

Asian

HCC

PB

PCR–RFLP

274/328

rs11614913, rs3746444

58

Z.Hu [78]

2008

Asian

breast cancer

PB

PCR–RFLP

1009/1093

rs11614913, rs3746444

59

Zhang [79]

2013

Asian

Acute lymphoblastic leukemia

PB

TaqMan

570/673

rs11614913

60

Zhao [80]

2013

Asian

HCC

PB

PCR–RFLP

235/281

rs11614913, rs3746444

61

Zhou [81]

2014

Asian

HCC

PB

PCR–RFLP

266/281

rs11614913, rs3746444

62

Zhu [82]

2011

Asian

Colorectal Cancer

HB

RT-PCR

573/588

rs11614913

HB, hospital based; PB, population based; HCC, hepatocellular carcinoma; ESCC, esophageal squamous cell carcinomar; PCR-RFLP, polymerase chain reaction–restriction fragment length.

polymorphism; PCR-CTPP, polymerase chain reaction with confronting two-pair primers; LDR, ligation detection reaction.

Statistical analysis

We first assessed the departure of frequencies of miRNA polymorphisms from expectation under Hardy-Weinberg equilibrium (HWE) for each study by using the goodness-of-fit test (chisquare or Fisher exact test) in controls. ORs corresponding to 95% CIs were calculated to access the strength of association between microRNA SNPs and cancer risks. Pooled ORs were obtained from combination of single study by heterozygote comparison (CT vs. CC for rs11614913; AG vs. AA for rs3746444), homozygote comparison (TT vs. CC for rs11614913; GG vs. AA for rs3746444), dominant model (TT + TC vs. CC for rs11614913; GG + AG vs. AA for rs3746444), recessive model (TT vs. CC + CT for rs11614913; GG vs. AG + AA for rs3746444) and allelic model (T vs. C for rs11614913; G vs. A for rs3746444) respectively. For each genetic comparison model, subgroup analysis according to ethnicity was investigated to estimate ethnic-specific ORs for Asian and Caucasian. Meanwhile stratified analyses by tumor type or control characteristics were also applied for each genetic comparison model.

Statistical heterogeneity between studies was checked by Cocharan’s chi-square based Q-test [18] and quantified by I2. If the P-value for heterogeneity was < 0.05, or if I2 was ≥ 50%, indicating substantial heterogeneity among studies, then a random-effect model using the DerSimonian and Laird method [31], which yielded wider CIs, was chosen to calculate the pooled OR; otherwise, a fixed-effect model using the Mantel-Haenszel method [19] was used. One-way sensitivity analyses were performed to assess the stability of the meta-analysis results [20]. Potential publication bias was estimated using Egger’s linear regression test by visual inspection of the Funnel plot. All P value < 0.05 was used as an indication of potential publication bias [21].

All statistical analyses were carried out with the review manager version 5.2 (Revman; The Cochrane Collaboration, Oxford, UK). All P values in the meta-analysis were two-sided, and P value less than 0.05 were considered significant.

RESULTS

Characteristics of the studies

In total, 462 published studies were obtained though literature search, including the PubMed, EMBASE and CNKI database. Under conditions prescribed by the inclusion and exclusion criteria, 122 eligible studies (Figure 1) were retrieved, because they were no detailed evaluation. During data extraction, 62 eligible studies [2283] were leaved, in which 56 and 37 studies were pooled for our meta-analysis, respectively (Figure 1). The characteristics of these selected studies are summarized in Table 1. Among all the included studies, there were 13 studies (hepatocellular cancer), 12 studies (breast), 7 studies (gastric), 6 studies (colorectal), 4 studies (lung), and 20 studies (other cancer types), and one (breast/ovarian cancer). There were 48 studies of Asian population, 14 studies of Caucasian population. Generally speaking, 56 studies included in our meta-analysis with 21958 cases and 26436 controls, which were ultimately analyzed for miR-196a2C.T (rs11614913), 37 studies including 13759 cases and 17946 controls for miR-499 A.G(rs3746444) .

Flow chart of the study selection process.

Figure 1: Flow chart of the study selection process.

Quantitative synthesis

miR-196a2C.T (rs11614913)

For miR-196a2C.T rs11614913 polymorphism, our mate-analysis contain 56 studies (21958 cases and 26436 controls). We observed the T allele frequency via different ethnicities (Asian: 0.93, 95% CI = 0.91–0.96; Caucasian: 0.96, 95% CI = 0.90–1.02).

In the overall analysis, our mate-analysis results manifested a statistically significant association between the miR-196a2C.T rs11614913 and the reduced risks of cancers (OR = 0.93, 95% CI = 0.91–0.96, PH < 0.00001 for T vs. C), homozygote comparison (OR = 0.88, 95% CI = 0.83–0.93, PH < 0.00001 for TT vs. CC), dominant model (OR = 0.92, 95% CI = 0.89–0.96, PH < 0.00001 for TT + CT vs. CC) and recessive model (OR = 0.94, 95% CI = 0.90–0.98, PH < 0.00001 for TT vs. CC + CT ) (Supplementary Table 1).

In subgroup analysis by cancer types, we found the significant associations between the miR-196a2C.T rs11614913 and colorectal cancer (OR = 1.21, 95% CI = 1.11–1.33, PH < 0.00001 for T vs. C; OR = 1.45, 95% CI = 1.21–1.74, PH < 0.00001 for TT vs.CC; OR = 1.25, 95% CI = 1.06–1.46, PH < 0.00001 for CT vs. CC; OR = 1.23, 95% CI = 1.05–1.43, PH < 0.00001 for TT + TC vs. CC; OR = 1.72, 95% CI = 1.50–1.98, PH < 0.00001 for TT vs. CC/TC); lung cancer (OR = 0.89, 95% CI = 0.82–0.97, PH = 0.008 for T vs.C; OR = 0.79, 95% CI = 0.67–0.94, PH = 0.26 for TT vs.CC; OR = 0.84, 95% CI = 0.74–0.96, PH = 0.2 for TT vs. CC + TC); gastric cancer (OR = 0.77, 95% CI = 0.69–0.85, PH< 0.00001 for T vs. C; OR = 0.54, 95% CI = 0.45–0.66, PH < 0.00001 for TT vs. CC; OR = 0.63, 95% CI = 0.52–0.75, PH < 0.00001 for CT vs. CC; OR = 0.66, 95% CI = 0.56–0.77, PH < 0.00001 for TT + CT vs. CC; OR = 0.76, 95% CI = 0.65–0.89, PH < 0.00001 for TT vs. CC + TC ). In addition, we also found the decreased risks in other cancer types (OR = 0.86, 95% CI = 0.82–0.90, PH < 0.00001 for T vs. C; OR = 0.90, 95% CI = 0.83–0.98, PH < 0.00001 for TT vs. CC; OR = 0.90, 95% CI = 0.84–0.97, PH < 0.00001 for TT + CT vs. CC; OR = 0.87, 95% CI = 0.81–0.93, PH < 0.00001 for TT vs. CC + TC ) (Figure 2). Subgroup analysis by the ethnicity revealed a significant association in the comparison of T vs.C (OR = 0.93, 95% CI = 0.91–0.96, PH < 0.00001), TT vs. CC (OR = 0.87, 95% CI = 0.82–0.92, PH < 0.00001), TT vs. CC + TC (OR = 0.91, 95% CI = 0.87–0.95, PH < 0.00001) in the Asian (Figure 3). Subgroup analysis by the source of control indicated a decreased risk in hospital based study, as showed in Supplementary Table 1.

Forest plot of cancer risk in different cancer types associated with miR-196a2 rs11614913 polymorphism for recessive model (TT + TC vs. CC).

Figure 2: Forest plot of cancer risk in different cancer types associated with miR-196a2 rs11614913 polymorphism for recessive model (TT + TC vs. CC).

Forest plot of cancer risk in different ethnicity associated with miR-196a2 rs11614913 polymorphism for recessive model (TT+TC vs. CC).

Figure 3: Forest plot of cancer risk in different ethnicity associated with miR-196a2 rs11614913 polymorphism for recessive model (TT+TC vs. CC).

miR-499 A.G rs3746444

For miR-499A.G rs3746444, our mate-analysis included 37 studies (13759 cases and 17946 controls). our mate-analysis results are showed in Supplementary Table 2. On the whole, we found that miR-499A.G rs3746444 was significantly associated with decreased risks of cancers under the G vs. A (OR = 1.14, 95% CI = 1.09–1.19, PH < 0.00001), GG vs. AA (OR = 1.20, 95% CI = 1.08–3.11, PH < 0.001), AG vs. AA (OR = 1.06, 95% CI = 1.01–1.11, PH < 0.00001 for), GG + GA vs. AA (OR = 1.16, 95% CI = 1.08–1.25, PH = 0.07) and GG vs. AG + AA (OR = 1.20, 95% CI = 1.09–1.33, PH < 0.00001).

In stratified analysis according to cancer types, we investigated the significant associations with breast cancer were only maintained under the G vs. A (OR = 1.18, 95% CI = 1.09–1.27, PH = 0.04), GG vs. AA (OR = 1.29, 95% CI = 1.08–1.56, PH = 0.04), GG + GA vs. AA (OR = 1.29, 95% CI = 1.08–1.54, PH = 0.02 ) and GG vs. AG + AA (OR = 1.18, 95% CI = 1.08–1.29, PH = 0.18). However, no statistically significant association was found in colorectal, lung, liver or other types cancers (Figure 4). Subgroup analysis according to ethnicity, significant associations with increased risks of cancers were found in Asian population (OR = 1.13, 95% CI = 1.08–1.19, PH < 0.00001 for G vs. A; OR = 1.19, 95% CI = 1.06–1.34, PH = 0.006 for GG vs. AA; OR = 1.17, 95% CI = 1.04–1.32, PH = 0.01 for GG + GA vs. AA; OR = 1.12, 95% CI = 1.06–1.19, PH < 0.00001 for GG vs. AG + AA), and in Caucasian (OR = 1.16, 95% CI = 1.06–1.26, PH < 0.00001 for G vs. A; OR = 1.29, 95% CI = 1.07–1.57, PH < 0.00001 for GG + GA vs. AA; OR = 1.16, 95% CI = 1.04–1.29, P H = 0.001 for GG vs. AG + AA) (Figure 5). According to study design, we found significant association between population-based studies with elevated risks of cancer (OR = 1.15, 95% CI = 1.10–1.20, PH < 0.00001 for G vs. A; OR = 1.22, 95% CI = 1.09–1.36, P H < 0.00001 for GG vs. AA; OR = 1.14, 95% CI = 1.08–1.20, PH < 0.00001 for GG + GA vs. AA; OR = 1.14, 95% CI = 1.08–1.20, PH < 0.00001 for GG vs. AG + AA), but the hospital-based studies was not observed a significant association summarized in Supplementary Table 2.

Forest plot of cancer risk in different cancer types associated with miR-499 rs3746444 polymorphism for recessive model (GG+GA vs. AA).

Figure 4: Forest plot of cancer risk in different cancer types associated with miR-499 rs3746444 polymorphism for recessive model (GG+GA vs. AA).

Forest plot of cancer risk in different ethnicity associated with miR-499 rs3746444 polymorphism for recessive model (GG+GA vs. AA).

Figure 5: Forest plot of cancer risk in different ethnicity associated with miR-499 rs3746444 polymorphism for recessive model (GG+GA vs. AA).

Sensitivity analysis

In the sensitivity analysis, each study involved in our meta-analysis was deleted the influence of the individual data on the coalescent ORs. The results of sensitivity analysis showed no obvious effects in overall population.

Publication bias

Begg’s funnel plot and Egger’s test were undertaken to evaluate the potential publication bias for this study. The shape of the Begg's funnel plots revealed no obvious asymmetry in all genotypes in overall population (Figures 6 and 7). The Egger’s test did not reveal publication bias (P > 0.05).

Funnel plot assessing evidence of publication bias (miR-196a2 rs11614913 (TT+TC vs. CC)).

Figure 6: Funnel plot assessing evidence of publication bias (miR-196a2 rs11614913 (TT+TC vs. CC)).

Funnel plot assessing evidence of publication bias (miR-499 rs3746444 (GG+GA vs. AA)).

Figure 7: Funnel plot assessing evidence of publication bias (miR-499 rs3746444 (GG+GA vs. AA)).

DISCUSSION

The most common form of genetic sequence variation, SNPs are affecting miRNAs sequence coding, splicing and expression such as miRNA gene in human genome, which can affect the susceptibility of cancer including Asian and Caucasian [83]. It was detected in much previous research effort that the role of SNPs is located in miRNA sequence of miR-196a2 C.T (rs11614913) and miR-499 A.G (rs3746444) and influences on the progression of cancers [84]. Recently, several studies have investigated genetic variants of the miRNA SNPs in cancer susceptibility, but conclusions of those studies remain inconclusive. In this study, we conducted a meta-analysis to evaluated the association between the overall cancers susceptibility and the two polymorphisms in miRNA (miR-196a2 C.T rs11614913, miR-499 A.G rs3746444).

For miR-196a2 C.T rs11614913 polymorphism, although previous studies have revealed no association between cancer susceptibility and the expression of miR-196a2 rs11614913 [8586]. Recently, the results of these meta-analysis studies have indicated a significant association between cancers susceptibility and miR-196a2 C.T rs11614913 [8792]. The SNPs of miR-196a2 have caused increasing attention because they influence on the maturation progression and mutation of miRNA, and they play potential roles in tumor development and progression(cell proliferation, differentiation, apoptosis, migration and invasion). The predecessor found that the genetic sequence variation in miR-196a2 C.T rs11614913 is located in the 3′ passenger (3p) mature sequence of miR-196a2, and this functional polymorphism is reportedly associated with the susceptibility of multiple kind of tumors (lung cancer and breast cancer). However, there was lower survival rates in small cell lung cancer, gliomas, gastric cancer, gallbladder, head and neck, esophageal, and HCC . During this interim of more than one year, some relevant case-control studies have been published, while conclusions of the relevant studies remain incomprehensive and inconsistent. For example, Dai et al. [32] results revealed that the miR-499 A.G rs3746444 polymorphisms are related to increased risks of breast cancer, while the miR-196a2 C.T rs11614913 polymorphisms are connected with reduced risks of breast cancer. Wei’s studies [93] suggested that the miR-196a2 C.T rs11614913 might not be connected with susceptibility to gastric cancer, while our study revealed that the miR-196a2 C.T rs11614913 decrease risks to cancer, especially colorectal and gastric cancer in Asians population, and that the miR-499A.G rs3746444 may increase risks of cancer. In subgroup analysis by cancer type, ours results indicate that significant association with risks of cancer was observed in colorectal, gastric and lung cancer. But we did not detect significant association in breast cancer. While Christensen et al. [94] showed the miR-196a2C.T rs11614913 may reduced incidence of breast cancer. In subgroup analysis according to ethnicity, we found the significant association with risks of cancer in Asian population, indicating a possible role of differences in genetic backgrounds between Asians and Caucasians.

As for miR-499A.G rs3746444 polymorphism, the pooled results studies revealed that the miR-499A.G rs3746444 was association with risks of cancer in multiple types of cancer [9597]. Several studies showed that a large amount of miRNAs are abnormally expressed in various cancers, and Zhang et al. [98] found that approximately 50% miRNA genes are located in cancer-associated regions, so miRNAs possibly exert a signifcant effect on the tumorigenesis. It was reported that studies have shown that the miR-499 rs3746444 can regulate the expression of SOX genes. The over-expression of SOX6 could reverse the anti-apoptosis effects of miR-499 A.G rs3746444 [99]. The abnormal expression of SOX genes can activate Wnt/β-catenin signaling pathway, which is associated with tumorigenesis and progression, so miR-499 A.G rs3746444 may play a decisive role in the occurring of cancer by altering SOX genes’ expression level. Moreover, the mate-analysis results from 37 studies revealed that the miR-499 rs3746444 G allele was revealed as a risk factor for cancers, in particular, for breast cancer or for in the Asian, which consistent with Hu’s results [100]. Therefore, The results illustrated that cancer types and district classification could cause different effects between miR-499 A.G rs3746444 polymorphism and risks of cancer.

To our knowledge, the source of the heterogeneity, including miR-196a2 C.T rs11614913 and miR-499A.G rs3746444, were mainly results from different ethnicity, different cancer types, different source of controls, different selection of subjects and sample size. Therefore, we evaluated the source of heterogeneity by cancer types, ethnicity, different selection of subjects and sample size. Nevertheless, our meta-analysis indeed exist some boundedness. Firstly, lack of relevant published data from the collected studies of potential gene-to-gene and gene-to-environment interactions, which may adjust risks of cancer. Secondly, potential heterogeneity was detected in some comparison, because they are unavoidable. Finally, publication bias existed in studies.

In conclusion, our meta-analysis indicated that the miR-196a2 C.T rs11614913 is significantly associated with a decreased risk of cancers, especially in the subgroup of colorectal, lung and gastric cancer, or Asians. Contrary to the above, the miR-499A.G rs3746444 most likely contributes to increased susceptibility of cancer in overall population, especially in breast cancer. Furthermore, more well-designed researches with large sample size are still necessary to elucidate the correlation between polymorphisms and different kinds of cancers risk.

ACKNOWLEDGMENTS

Wanjun Yan and Shuqun Zhang designed and wrote this article. Wanjun Yan and Shuqun Zhang screened out relevant articles and extracted data. Wanjun Yan, Xiaoyan Gao, and Shuqun Zhang analyzed data. Wanjun Yan and Shuqun Zhang revised this article.

CONFLICTS OF INTEREST

The authors have no conflicts of interest in this work.

GRANT SUPPORT

This study was supported by National Natural Science Foundation, People’s Republic of China (No. 81274136) and Program for New Century Excellent Talents in University of China (No. NCET-110439).

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